Marketing Data Scientist

Building data science models to inform marketing decisions means translating customer behavior into actionable segments, predictions, and attribution insights. Your work spans customer segmentation, lifetime value modeling, churn prediction, propensity scoring, and the attribution models that connect marketing spend to revenue.

The workflow alternates between model development and stakeholder communication. Some weeks are heads-down analytical work — building datasets, training models, validating results. Others involve presenting findings to marketing leaders who need recommendations, not p-values. The harder craft is getting marketers to actually use the models you build rather than reverting to intuition.

The persistent challenge is building models that work with messy marketing data. Customer journeys span multiple channels and devices, attribution is inherently ambiguous, and the experimental conditions that make good science possible are often at odds with what the marketing team wants to run.